Method and system for obtaining reference signals for vehicles control systems and corresponding control system

ABSTRACT

A method for obtaining reference signals for vehicle control systems, in function of a vehicle geographical position along a travel route, includes providing data relating to the vehicle and data relating to a route to travel, and determining a vehicle driving force reference signal and a vehicle speed reference signal through a first optimisation process configured to optimise the driving force along the travel route. An engaged gear reference signal, in function of the positions of the vehicle along the travel route, is determined through a second optimisation process configured to optimise fuel consumption of the vehicle along the travel route. The second optimisation process is subsequent to the first optimisation process, and the data relating to the travel route, as well as the driving force reference signal and the speed reference signal, is received as input, determined through the first optimisation process.

BACKGROUND

The present invention relates to a method and a system for obtaining reference signals for vehicles control systems.

The present invention also relates to a control system for vehicles wherein such a method and such a system for obtaining reference signals are advantageously applicable.

STATE OF THE ART

In the state of the art systems for controlling the cruising speed of vehicles, so-called “Cruise control” systems, are known which facilitate the driving, by allowing an automatic adjustment of a vehicle speed, compatible with its set-up conditions, with the aim of reducing the consumption thereof.

Such systems, which have become part of the standard equipment, for example in transport vehicles, in their more sophisticated version, in order to maintain a set cruising speed, in addition to acting on the gas control, can act on the vehicle braking devices (for example the retarder, the traditional friction brakes or the engine brake) as well as on the control of the gearbox, in the case of automatic transmission.

In some cases, traditional systems for determination and control of the cruising speed of a vehicle does not use information on the conditions of the route to travel, and therefore, in some contexts, they are found to be not optimal for speed adjustment.

Other prior art systems take, however, into account the characteristics of the route to travel, like for example the systems described in the documents WO 2012/088537, WO 2010/144029, WO 2010/144031 and WO 2013/095234.

Document WO 2012/088537 teaches a method for determining the recommended operative conditions of a vehicle, which minimize the fuel consumption, taking into consideration also the properties of the route to be travelled. The method disclosed in WO 2012/088537 comprises two steps: a first offline step, wherein a coarse evaluation of the pattern of the vehicle speed and gear state is performed, based on the route to be travelled, and a second on-line step, refining the pattern on the vehicle speed and gear state, based on the coarse evaluation of the pattern of the vehicle speed and gear state resulting from the first offline step. The first offline step and the second on-line step above optimize the same cost function.

The systems of other documents, which are focused on the determination of a reference speed of a vehicle, according to the assessment of the conditions of a road “horizon”, are however very heavy from a computational point of view.

There is therefore the need to develop a method for obtaining reference signals for vehicles control systems, which is alternative and solves the above mentioned drawbacks of the conventional methods.

GOALS OF THE INVENTION

The main object of the present invention is to improve the state of the art in the field of vehicles in general, and more particularly in the field of systems for controlling the speed of such vehicles.

More particularly, it is an object of the present invention to provide a method for obtaining reference signals for vehicles control systems that is alternative with respect to traditional methods.

Another object of the present invention is to provide a method for obtaining reference signals for control systems of a vehicle, which is fast to be implemented.

Yet another object of the present invention is to provide a method for obtaining reference signals for control systems of a vehicle, which requires more limited computational resources with respect to traditional methods, thereby ensuring high reliability and efficiency.

Another object of the present invention is to provide a system for obtaining reference signals for control systems of a vehicle, which is easy to implement at competitive costs.

Not the last object of the present invention is to provide a control system for vehicles which is alternative to traditional systems.

It is a specific object of the present invention a method for obtaining reference signals for control systems of a vehicle, according to claim 1.

It is also a specific object of the present invention a system for obtaining reference signals for control systems of a vehicle, according to claim 13.

It is a further specific object of the present invention a control system for vehicles according to claim 14.

It is furthermore a specific object of the present invention a set of one or more computer programs according to claim 16.

It is yet a further specific object of the present invention a set of one or more computer readable media according to claim 17.

The dependent claims refer to further preferred and advantageous embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be now described, for illustrative but not limiting purposes, according to its preferred embodiments, with particular reference to the drawings in the accompanying Figures, wherein:

FIG. 1 shows a first flow diagram of the method according to the present invention;

FIG. 2 is an example block diagram of a system configured to perform the method of FIG. 1;

FIG. 3 shows an example block diagram of first optimisation process of the method of FIG. 1;

FIG. 4 is a representation of an approximation of the driving force of a vehicle, according to the method of the present invention;

FIG. 5 shows an example block diagram of an iterative optimization procedure, carried out during the first optimisation process of the method according to the invention;

FIG. 6 shows an example block diagram of second optimisation process of the method of FIG. 1;

FIG. 7 is an exemplary representation of one between various versions of a system configured to implement the method according to the present invention;

FIG. 8, shows the trend of the speed profiles (b), fuel consumption (c) and gear switching (d), respectively, obtainable along a route shown in (a) with a traditional method (CC) for adjusting the speed of a vehicle and with the method of FIG. 1 (OPT); and

FIG. 9 shows some experimental results supporting the efficacy of the present invention.

EMBODIMENTS OF THE INVENTION

With reference to the accompanying Figures, in particular at FIGS. 1 and 2 it will be noted that the method for obtaining reference signals for control systems of a vehicle according to the present invention is generally indicated with reference 1 and comprises at least the following operating steps of:

-   -   providing (100) at least data relating to said vehicle V and at         least data relating to a route to travel (step 100);     -   Determine, on the basis of said data, at least one reference         signal of the driving force F=F(s) and a reference signal of the         speed ν=ν(s) for the vehicle V, in function of the position s of         the vehicle itself, along the route to travel, through a first         optimisation process (step 200) (for which the speed ν=ν(s) is         generally not a constant cruising speed); and     -   determine at least one reference signal of the engaged gear γ(s)         (or of the corresponding gear changes u_(sh)(s)) of the vehicle         V, in function of the position s of said vehicle along the route         to travel. Through a second optimisation process (step 300).

The second optimisation process (step 300) is subsequent to the first optimisation process (step 200) and receives as input both the data relating to the vehicle V and the data relating to the route to travel, and the reference signal of the driving force F and the speed reference signal ν, processed during the first optimisation process (step 200).

With particular reference to step 100 of the preferred embodiment of the method according to the present invention (see FIG. 3), it envisages to provide information relating to the vehicle, which may include: information relating to its mass, to its aerodynamic resistance and to the rolling friction (for example in the form of corresponding coefficients), to the pattern of the maximum torque developed by the engine of the vehicle and, as will be seen below, information relating to fuel flow maps (for example, as function of the torque developed by the engine, and the speed of the same engine) and the transmission ratios.

The step 100 of the method according to the present invention also provides for the supply of certain data relating to the route that the vehicle V will travel, including, for example:

-   -   a current geographical position of the vehicle, provided for         example by means of a suitable Global Positioning System (GPS,         GLONASS or other similar system),     -   a destination geographical position,     -   information relating to the route between the starting         geographical position of the vehicle (which is advantageously         the geographical position of departure of the route to travel)         and the destination geographical position (including the path of         the roadway of the route to travel), such as the slope or the         altitude of the roadway that the vehicle must travel,     -   data corresponding to the speed limits along the route, as well         as     -   a desired arrival time, inserted for example by the driver of         the vehicle, and     -   other optional data such as, for example, an average speed, the         maximum and minimum speed desired by the driver as well as the         degree of adherence to those speeds.

The information that can be inputted by the driver are used by the method according to the present invention, as will be seen hereinafter, to take into account also his driving style and the conditions of the road to travel.

These data are used to perform a first optimisation process (step 200), after suitable treatment (for example comprising a filtering step and re-sampling and/or other treatment of any suitable type), which provides for determining a reference signal of the vehicle speed ν (indicated in FIG. 7 with reference V) that optimizes the fuel consumption during the route to travel, as well as a reference signal of the driving force F of the vehicle along that route.

More in particular, said first optimisation process comprises an analytical process of minimization of a first cost function which is a cost function J(F) of the driving force F, required to drive the vehicle V of mass m along a route having global length S.

The minimization of the cost function J(F) with respect to the drive force F is given by the relation:

$\begin{matrix} {{\min\limits_{F}\left\{ {J(F)} \right\}} = {\min\limits_{F}\left\{ {\int_{0}^{S}{F^{2}{ds}}} \right\}}} & (1) \end{matrix}$

with the following state equation in the time domain:

$\begin{matrix} {{m\frac{dv}{dt}} = {F - F_{grade} - F_{drag} - F_{roll}}} & (2) \end{matrix}$

and the constraints:

$\begin{matrix} {v_{\min} \leq v \leq v_{\max}} & (3) \\ {F \leq F_{\max}} & (4) \\ {{{\int_{0}^{S}{\frac{1}{v}{ds}}} \leq T_{\max}} = \frac{S}{{\overset{\_}{v}}_{des}}} & (5) \end{matrix}$

wherein ν is the vehicle speed, S is, as said above, the global length of the route to travel ν_(min) is the minimum speed and ν_(max) is the maximum speed of the vehicle (optionally provided by the driver and/or from the data relating to the route the vehicle V will travel, such as those relating the speed limits), F_(max) is the available maximum driving force of the vehicle (where the maximum driving force F_(max) is the control variable), T_(max) is a desired maximum travel time (optionally provided by the driver) and ν _(des) is the set desired average speed, even this optionally provided for by the driver of the vehicle V.

The minimization (1) of the cost function J(F) is aimed at minimising the energy used to drive the vehicle having mass m along a route of finite length S, given the constraints for the state equation (2) relating to the speed ν, maximum driving force F_(max) and desired maximum travel time T_(max) mentioned above in equations (3) to (5). Moreover, the state equation (2) also takes into account the fact that the vehicle V is subject to the following exogenous forces:

F _(grade) =m g sin(α)   (6)

F _(drag)=½ρ_(air) C _(x) Aν ²   (7)

F _(roll) =m g cos(α)[C _(r0) +C _(r1ν) ²]  (8)

Due to the slope of the road along which the vehicle is travelling (F_(grade)), to its aerodynamic resistance F_(drag) and to the rolling friction F_(roll), respectively, which depend, other than the global mass m of the vehicle V, also from the gravitational acceleration g, from the angle corresponding to the slope of the road (which in general can be variable along the route, for which α=α(s)), from the ambient air density ρ_(air) (which in general can be variable along the route, for example because the route runs through different altitudes for which ρ_(air)=ρ_(air) ^((s))), from the aerodynamic drag coefficient C_(x), from the frontal area A of the vehicle V, from the first coefficient C_(r0) of rolling resistance (which in general can be variable along the route, for example due to the progressive wear of the tires and/or the different type of asphalt or soil and/or the potentially different weather conditions, for which C_(r0)=C_(r0)(S)) and from the second coefficient C_(r1) of rolling resistance (which in general can be variable along the route, for example as a result of the progressive wear of the tires and/or the different type of asphalt or soil and/or the potentially different weather conditions, so that C_(r1)=C_(r1)(s)).

Since the cost function to J(F) to be minimized and the constraint relating to maximum travel time T_(max) to be met reflect both objectives in the space domain, the status equation (2) can be rewritten in the space domain (see equation (9) below) and the state variable ν (i.e. the speed), is replaced by the kinetic energy K so that the equations (2)-(5), using the definitions of exogenous forces acting on the vehicle V, given by equations (6)-(8) can be rewritten as follows:

$\begin{matrix} {\frac{dK}{ds} = {F - {m\mspace{14mu} g\mspace{14mu} {\sin (\alpha)}} - {\frac{\rho_{air}\mspace{14mu} C_{x}\mspace{14mu} A}{m}K} - {m\mspace{14mu} g\mspace{14mu} {{\cos (\alpha)}\left\lbrack {C_{r\; 0} - {C_{r\; 1}\frac{2\mspace{14mu} K}{m}}} \right\rbrack}}}} & (9) \\ {\mspace{76mu} {{\frac{1}{2}m\mspace{14mu} v_{\min}^{2}} = {{K_{\min} \leq K \leq K_{\max}} = {\frac{1}{2}m\mspace{14mu} v_{\max}^{2}}}}} & (10) \\ {\mspace{76mu} {F \leq F_{\max}}} & (11) \\ {\mspace{76mu} {{{\int_{0}^{S}{\sqrt{\frac{m}{2\mspace{14mu} K}}{ds}}} \leq T_{\max}} = \frac{S}{{\overset{\_}{v}}_{des}}}} & (12) \end{matrix}$

where K_(min) is the kinetic energy associated with the minimum speed ν_(min) and K_(max) is the kinetic energy associated with the maximum speed ν_(max).

The integral constraint (12), to be suitably treated, requires the introduction of the additional state variable t(s), i.e. the time required to travel the space s, thus resulting in the addition of a further constraint equation:

$\begin{matrix} {\frac{dt}{ds} = \sqrt{\frac{m}{2\mspace{14mu} K}}} & (13) \end{matrix}$

according to which, the integral constraint (12) can be explicitly rewritten as:

t(S)=T _(max)   (14)

This stated, based on the method according to the present invention, the minimization of the cost function J(F), while respecting the constraints described above can be obtained according to the method of the present invention, by means of the minimization of the integral of the Lagrangian, function of the driving force F, given by:

$\begin{matrix} {{{\mathcal{L}(F)} = {{\int_{0}^{S}{\left\{ {F^{2} + {{a(s)}\left\lbrack {\frac{dK}{ds} - F + {m\mspace{14mu} g\mspace{14mu} {\sin (\alpha)}} + {\frac{\rho_{air}\mspace{14mu} C_{x}\mspace{14mu} A}{m}K} + {m\mspace{14mu} g\mspace{14mu} {\cos (\alpha)}\left( {C_{r\; 0} - {C_{r\; 1}\frac{2\mspace{14mu} K}{m}}} \right)}} \right\rbrack} + {g_{1}(K)} + {g_{2}(K)} + {g_{3}\left( {F,{v(K)}} \right)} + {\lambda \left( {\frac{dt}{ds} - \sqrt{\frac{2\mspace{14mu} K}{m}}} \right)}} \right\} {ds}}} + {b_{1}\left( {{K(0)} - K_{0}} \right)} + {b_{2}\left( {{t(0)} - 0} \right)} + {\phi \left( {{t(S)} - T_{\max}} \right)}^{2}}}\ } & (15) \end{matrix}$

where the penalisation functions g₁, g₂ and g₃ are defined by the following equations:

$\begin{matrix} {{g_{1}(K)} = \left\{ \begin{matrix} {c_{1}\left( {K - K_{\max}} \right)}^{2} & {{{if}\mspace{14mu} K} > K_{\max}} \\ {0\mspace{135mu}} & {{{if}\mspace{14mu} K} \leq K_{\max}} \end{matrix} \right.} & (16) \\ {{g_{2}(K)} = \left\{ \begin{matrix} {c_{2}\left( {K_{\min} - K} \right)}^{2} & {{{if}\mspace{14mu} K} < K_{\min}} \\ {0\mspace{135mu}} & {{{if}\mspace{14mu} K} \geq K_{\min}} \end{matrix} \right.} & (17) \\ {{g_{3}\left( {F,v} \right)} = \left\{ \begin{matrix} {c_{3}\left( {F - {F_{\max}(v)}} \right)}^{2} & {{{if}\mspace{14mu} F} > F_{\max}} \\ {0\mspace{160mu}} & {{{if}\mspace{14mu} F} \leq F_{\max}} \end{matrix} \right.} & (18) \end{matrix}$

where c₁, c₂ and c₃ are penalisation coefficients.

In the equations above, the available maximum driving force F_(max), according to the method of the present invention, can be obtained for example by means of an analytical approximation function of the envelope of the maximum driving force for each gear, as shown in FIG. 4, given by the sum of three Gaussian functions according to the relation:

$\begin{matrix} {{F_{\max}(v)} = {\sum\limits_{j = 1}^{3}\; {\zeta_{j}e^{- {(\frac{v - \eta_{j}}{\xi_{j}})}^{2}}}}} & (19) \end{matrix}$

wherein ν is the vehicle speed and ζ_(1 . . . 3), η_(1 . . .) , ξ_(1 . . . 3) are nine constant coefficients.

It must be considered that the Gaussian functions necessary to the representation of the envelope of the maximum driving force for each gear (i.e. as a function of speed ν) could be any other number P greater than or equal to 2 (e.g. 2, 4 or 5), and the number of the constant coefficients ζ_(1 . . . P)η_(1 . . . P), ξ_(1 . . . P), is equal to 3 times P. Moreover, it must be kept in mind that the available maximum driving force F_(max) could be represented in analytical form with other basic functions rather than with Gaussian functions, for example by using a piecewise linear function.

In the integral of the Lagrangian (15), the presence of the constraints of equality (9) and (13), according to the method of the invention requires the introduction of added fields a(s) and λ; in addition, in the integral of the Lagrangian (15) φ is the amplitude of a quadratic penalisation term, to meet the constraint (14), while b₁ and b₂ are Lagrange multipliers, which however do not have any operational significance.

The method according to the invention performs an algorithm of computation of the Lagrangian gradient (15). In the preferred embodiment of the method according to the invention, that algorithm is adjoint-based iterative and, as shown in FIG. 5, follows the method of the conjugate gradient. That algorithm provides for the computation of the gradient of the integral of the Lagrangian (15)

$\frac{\delta\mathcal{L}}{\delta \; F}$

(step 250) using the added field, and the minimization of the integral of the Lagrangian is in fact performed by the method of the conjugate gradient by iteratively using the gradient

$\frac{\delta\mathcal{L}}{\delta \; F}$

thus calculated, to determine the search direction along which to search for the minimum (step 260). In particular, the penalisation coefficients c₁, c₂ and c₃ of equations (16)-(18) can be constant or optionally progressively increased at successive iterations; similarly, the amplitude φ of the quadratic penalisation term can be a constant value or optionally modifiable, dynamically, at successive iterations. Once the search direction is determined, the algorithm provides to perform the line minimization (step 270) and repeat the iterations until at least one stop criterion of the same iterations is met (step 280), which indicates that a minimum of the Lagrangian integral has been reached.

Since this is an iterative method, it provides to give likely initial values K(s) and t(s) for the variable F(s), for example by solving equations (9) and (13) (step 251).

For the gradient computation, the added field is then determined λ (step 252), by solving the following added equation:

$\begin{matrix} {\frac{\delta\mathcal{L}}{\delta \; t} = \left. 0\rightarrow\left\{ \begin{matrix} {{\frac{d\; \lambda}{ds} = 0}\mspace{155mu}} \\ {{\lambda (S)} = {2{\phi \left( {{t(S)} - T_{\max}} \right)}}} \end{matrix} \right. \right.} & \left. 20 \right) \end{matrix}$

derived from the first disturbance of the Lagrangian integral (15).

The solution of the above differential equation is trivial and highlights that λ must be constant and equal to its final condition.

At the next step (step 253), the field a(s)is determined, by solving the following the second added equation:

$\begin{matrix} {\frac{\delta\mathcal{L}}{\delta \; K} = \left. 0\rightarrow\left\{ \begin{matrix} {{\frac{da}{ds} = {{{- 2}{a\left( {{\frac{1}{2}\frac{\rho_{air}\mspace{14mu} C_{x}\mspace{14mu} A}{m}} + {C_{r\; 1}\mspace{11mu} g\mspace{14mu} {\cos (\alpha)}}} \right)}} + \frac{{dg}_{1}}{dK} + \frac{{dg}_{2}}{dK} + {\frac{1}{2}\lambda \sqrt{\frac{m}{2}}\frac{\sqrt{1}}{K^{3}}}}}\;} \\ {{{a(S)} = 0}\mspace{661mu}} \end{matrix} \right. \right.} & (21) \end{matrix}$

from which (step 254) the gradient of the Lagrangian integral is obtained based on the relation:

$\begin{matrix} {\frac{\delta\mathcal{L}}{\delta \; F} = {{- a} + {2F} + \frac{{dg}_{3}}{dF}}} & (22) \end{matrix}$

The gradient of the Lagrangian integral thereby calculated is used in step (step 260) for determining the search direction ρ_(k), in the space of decision variables, wherein, at the k-th iteration, the search direction ρ_(k) is determined based on the relation:

$\begin{matrix} \left\{ \begin{matrix} {{p_{k} = {{- \frac{\delta\mathcal{L}}{\delta \; F}}_{k}}}\mspace{101mu}} & {{{if}\mspace{14mu} k} = 1} \\ {p_{k} = {{- \frac{\delta\mathcal{L}}{\delta \; F}}_{k}{{+ \beta_{k}}p_{k - 1}}}} & {{{if}\mspace{14mu} k} > 1} \end{matrix} \right. & (23) \end{matrix}$

wherein:

-   -   in the first iteration (k=1), as search direction the opposite         of the gradient, i.e. the steepest descent direction is used,         and     -   In subsequent iterations (k>1) the conjugate gradient is         determined, where β represents the momentum of the search         direction at the previous iteration and is determined by the         formula of Polak-Ribiére:

$\begin{matrix} {\beta_{k} = {\max \left( {0;\frac{\left( {{- \frac{\delta\mathcal{L}}{\delta \; F}}_{k}} \right)^{T}\left( {{- \frac{\delta\mathcal{L}}{\delta \; F}}_{k}{{+ \frac{\delta\mathcal{L}}{\delta \; F}}_{k - 1}}} \right)}{\left( {{- \frac{\delta\mathcal{L}}{\delta \; F}}_{k - 1}} \right)^{T}\left( {{- \frac{\delta\mathcal{L}}{\delta \; F}}_{k - 1}} \right)}} \right)}} & (24) \end{matrix}$

IT is to be considered that β can be determined by other formulas, such as for example the Fletcher-Reeves or Hestenes-Stiefel formula.

Once the search direction has been determined, the line minimization is performed by calculating (step 270) the length of the pitch (h_(k)) of the driving force F that ensures the maximum reduction of the cost function

F _(k+1) =F _(k) +h _(k)ρ_(k)   (25)

by iteratively solving the following not linear equation, for the scalar h,

$\begin{matrix} {\frac{\partial{\mathcal{L}\left( {F + {h\mspace{14mu} p}} \right)}}{\partial h} = {{\frac{\partial{\mathcal{L}\left( {F + {h\mspace{14mu} p}} \right)}}{\partial\left( {F + {hp}} \right)}\frac{\partial{\mathcal{L}\left( {F + {h\mspace{14mu} p}} \right)}}{\partial h}} = {\left( {{2\left( {F + {hp}} \right)} - a + \frac{{dg}_{3}}{dF}} \right) = 0}}} & (26) \end{matrix}$

for example, by the method of Brent described by R. P. Brent in “Chapter 4: An algorithm with Guaranteed Convergence for finding a zero of a Function”, Algorithms for minimization without derivatives, Englewood Cliffs, N.J.: Prentice-Hall, 1973, ISBN 0-13-022335-2, or also by other methods such as Dekker algorithm.

Once the length h_(k) of the pitch is determined, the control variable F, is updated (step 271) and at step 280 it is checked whether a criterion for stopping the iteration is met or not.

The stop criterion of the algorithm according to the preferred embodiment of the method of the present invention is given by:

$\begin{matrix} \left\{ \begin{matrix} {\frac{{J\left( F_{k} \right)} - {J\left( F_{k - 1} \right)}}{J\left( F_{k - 1} \right)} \leq ɛ_{J}} \\ {\frac{{t(S)}_{k}{{- {t(S)}}_{k - 1}}}{{t(S)}_{k - 1}} \leq ɛ_{t}} \end{matrix} \right. & (27) \end{matrix}$

wherein ε_(j) and ε_(t) are two predetermined threshold values.

As shown, the stop criterion is given by two conditions: a first condition, linked to the cost function J(F), which is considered to be minimized when the relative variation of the same between an iteration and the previous one is less than or equal to ε_(j); the second stop criterion, which also must be met in order for the iterations to stop, concerns the duration t(S) of the whole route, whose variation between an iteration and the previous one cannot be greater than ε_(t).

Based on the calculations mentioned above, the first optimization process (step 200 of FIGS. 1 and 5) provides as output a reference signal of the speed ν(s) of vehicle V and a reference signal of the driving force F(s), as a function of its position s along the route to travel.

Based on the results outputted from the first optimisation process, the method according to the present invention provides for performing a second optimisation process (step 300 of FIG. 1) of a second cost function, which is different from the cost function of the first optimisation process, by the method of the dynamic programming (also known as dynamic programming or dynamic optimization), which is a cost function J₂ of fuel consumption. The cost function J₂is expressed as the sum, on M length segments Δs₂ that make up the route of length S, of the costs due to the fuel consumption along each segment i-th having length Δs₂ and penalisation factors, aimed at penalising the gear changes, wherein an additional penalisation is applied when a gear change occurs on uphill sections of the route.

The block diagram of FIG. 6 schematically illustrates the second optimisation process of the preferred embodiment of the method according to the present invention. In particular, the second optimisation process, based on the reference signal of the speed ν(s) of the vehicle and a reference signal of the driving force F(s) obtained from the first optimization process (step 200 of FIGS. 1 and 5) of the method and the data mentioned above relating to the type of the route to travel, in particular with reference to the slope or altitude of the roadway to travel and the vehicle V (mass, fuel flow maps, transmission ratios or the like).

More in detail, this second optimisation process performs the minimization of the cost function J₂ as a function of the gear changes γ, associated with the fuel consumption of the vehicle, and given by the relation:

$\begin{matrix} {{\min\limits_{\gamma_{i} \in \Gamma_{i}}\left\{ J_{2} \right\}} = {\min\limits_{\gamma_{i} \in \Gamma_{i}}\left\{ {\sum\limits_{i = 1}^{M}\; \left( {\frac{{W_{f,i}\left( \gamma_{i} \right)}\Delta \; s_{2}}{v_{i}} + {{\mu_{1,i}\left( {\alpha_{i},m} \right)}{u_{{sh},i}}} + {\mu_{2}{u_{{sh},i}}}} \right)} \right\}}} & (28) \end{matrix}$

with the following state equation:

γ_(i+1)=γ_(i) +u _(sh,i)   (29)

and the constraints that follow:

γ_(i)ϵΓ_(i)   (30)

Γ_(i):n_(e,min)≤n_(e)(γ_(i), K_(i))≤n_(e,max)   (31)

u _(sh,i)ϵ[−2; −1, 0; +1; +2]  (32)

wherein:

$\begin{matrix} {M = \frac{S}{\Delta \; s_{2}}} & (33) \\ {\mu_{1,i} = \left\{ \begin{matrix} {m \cdot \alpha_{i}} & {{{if}\mspace{14mu} \alpha_{i}} > 0} \\ {0\mspace{40mu}} & {{{if}\mspace{14mu} \alpha_{i}} \leq 0} \end{matrix} \right.} & (34) \end{matrix}$

and wherein:

-   -   u_(sh,i) is the gear change signal in segment i and represents         the control variable;     -   M is the number of addenda of the cost function J₂ (equal to the         number of length segments Δs₂ that compose the route of length         S);     -   W_(f,i) Is the fuel flow rate (mass/time) in segment i;     -   Δs₂ is the constant length of each one of the M segments (even         if in other embodiments of the method according to the invention         the length Δs_(2,i) of each segment i can be in general         different from the lengths of the other segments);     -   μ_(1,i) is a first penalisation coefficient that is variable as         a function of the mass m of the vehicle and the slope α_(i) of         the segment i;     -   μ₂ is a second penalisation coefficient which is constant;     -   γ_(i) Is the gear of the vehicle in the segment i, comprised         between the set of gears Γ_(i), and represents the state         variable (in general, the set of gears Γ_(i) can change section         by section),     -   n_(e) Is the speed of the engine, between a maximum value and a         minimum value n_(e,max) and n_(e,min).

It must be considered that, similarly to the prior art systems, when exceptional or emergency conditions occur that require the engagement of gears outside the set Γ_(i) or the operation of the brakes, both in automatic mode and for direct intervention of the driver, the method according to the invention stops its execution (i.e. is turned off).

A sufficiently high value must be assigned to penalisation factors μ_(1,i) and μ₂ so that the gear changes that are not needed are properly penalised.

Moreover, it should be noted that equation (32) limits the set of possible operations between: maintain the gear (0), increase one or two gears (+1, +2) or decrease one or two gears (−1, −2).

The term W_(f,i) the first contribution of the addenda of the sum of the cost function J₂, can be expressed as a function of the torque T_(e) developed by the engine and the speed n_(e) of the same engine, according to the fuel flow map W_(f)(T_(e), n_(e)) of the engine of the vehicle. As it can be easily understood, other engine parameters can be alternatively or additionally used to determine the fuel flow rate W_(f,i), in a totally obvious way for the person skilled in the art.

According to the present invention, however, the speed n_(e) and the torque T_(e) developed by the engine can be expressed as a function of the gear γ through the relations:

$\begin{matrix} {{T_{e}\left( {F,\gamma} \right)} = \frac{F\mspace{14mu} R_{w}}{i(\gamma)}} & (35) \\ {{n_{e}\left( {v,\gamma} \right)} = \frac{v\mspace{14mu} {i(\gamma)}}{R_{w}}} & (36) \end{matrix}$

where i(γ) is the global transmission ratio, with the engaged gear γ, and R_(w) is the radius of the wheel of the vehicle V.

From equations (35) and (36) it is therefore clear that, once known F(s) and ν(s), supplied in output from the first optimisation process of the method according to the present invention, the cost function J₂, different from the cost function of the first optimisation process, associated with the fuel consumption during the route, is a function of only the engaged gear γ_(i) in each segment of the route to travel.

Therefore, in output from the second optimization process of the method according to the present invention, there is obtained a reference signal of the engaged gear γ(s) (or the gear changes u_(sh)(s)) of the vehicle V, along the route to travel; advantageously, although the signal of the engaged gear γ_(i) refers to the segment i, nevertheless the reference signal of the engaged gear γ(s) (or the gear changes u_(sh)(s)) is provided as a control signal at a certain travelled distance s.

Having said that, in view of the above, it is clear how the method according to the present invention, that implements two successive processes of optimization, each one based on a cost function that is different from that of the other optimization process, is able to obtain reference signals of the speed ν(s) of a vehicle V and the gear engaged γ(s) (or the gear changes u_(sh)(s)) along the entire route travel so that, given the current position s of the vehicle V along the way, it will be possible to determine in a completely unique way the corresponding values of the speed ν(s) and of the reference gear γ(s).

A system for obtaining reference signals for control systems of a vehicle V, as function of the geographic position of the same vehicle along a route travel will be now described purely by way of example and with reference to FIG. 2.

Such a system 2 comprises at least one device 3 for collecting data, configured to output data relating vehicle V and a route to travel, as well as any data that can be inserted by the driver of the vehicle V and data related to the engine of the vehicle V. In FIG. 2, said at least one device 3 comprises: a memory 3 a that stores data related to the vehicle V, such as the total mass m of the vehicle V (the value of which can possibly be updated by weight sensors positioned on the specific components of the vehicle V), an aerodynamic resistance coefficient C_(x), frontal area A of the vehicle V, the first and the second coefficient C_(r0) and C_(r1) rolling resistance of the tires of the vehicle V (which can be updated based on the progressive wear of the tires); a satellite navigator 3 b (provided for example with GPS sensor and/or GLONASS) that provides data relating to the position of the vehicle and the route to travel, such as the current position of the vehicle, a geographical destination position, information relating to the route between the position of the vehicle V and the destination (such as the slope or the altitude of the roadway that the vehicle V must travel), data corresponding to the speed limits along the way, and optionally information relating to traffic conditions and the weather condition; an input/output interface 3 c through which the driver of the vehicle can enter user data, such as for example a desired arrival time, an average speed, the maximum and minimum speed required by the driver, and optionally a degree of adherence to the speed set by the driver; a 3 d system configured to provide in output data relating to the engine, optionally comprising one or more sensors 3 d configured to provide in output data related to the torque developed by the engine and/or the speed of the engine itself (such sensors may be those already present in the vehicle and the data can be retrieved from the communication board system, such as the CAN-BUS) and a memory which stores the pattern of the maximum torque developed by the engine of the vehicle, fuel flow maps (for example as function of the torque developed by the engine and the engine speed) and the transmission ratios. Other embodiments of the system according to the invention can comprise additional sensors, for example for detecting a ambient air density and/or weather conditions.

The system 2 also comprises at least one first module 4 for optimizing the driving force, operatively connected to said at least one device 3, and configured to determine, by means of a first optimisation process such as that described above with reference to FIG. 5, a driving force signal F(s) and a speed signal ν(s) to the vehicle, based on the position s of the vehicle V, along the route.

The system 2 further comprises at least one second module 5 for optimizing the fuel consumption, arranged downstream of the first module 4 for optimizing the driving force and operatively connected to said first module 4 and to said at least one device 3 for collecting data. The second optimization module 5 is configured to determine a reference signal of the reference gear γ(s) (or the gear changes u_(sh)(s)) of the vehicle V, along the route to be travelled, through a second optimisation process.

In particular, the first and the second module 4 and 5 can be implemented through two respective processing units, provided for example with at least one microprocessor or micro-controller, or through a single processing unit, provided for example with at least one microprocessor or micro-controller, configured to perform the method according to the invention.

Said at least one device 3 is configured to provide the first module 4 and the second module 5 with the data illustrated above with reference to the optimization processes of FIGS. 3, 5 and 6.

The first and the second module 4 and 5 receive from the satellite navigator 3 b the information regarding to the route to travel i.e. the slope or the altitude of the roadway along the route to travel, which route is set by the driver by means of the interface input/output 3 c, and speed limits provided along that route (and optionally the information regarding the traffic conditions and the weather condition) based on which the first and the second module 4 and 5 perform the first and the second optimization process, respectively, as shown in FIGS. 3 and 6 (wherein such information are cumulatively indicated with the reference number 30).

The information 30 provided by the satellite navigator 3 b may also have been stored beforehand in the same satellite navigator, or may be processed on case by case basis, optionally through a connection by means of a wireless radio communication network (wireless) according to one communication protocol suitable for one or more remote servers or to a remote processing unit.

FIGS. 3 and 6 also show the information 35 supplied by the memory 3 a and by the system 3 d to the first and the second module 4 and 5 (even if the information 35 provided by the system 3 d is actually used only by the second module 5), i.e. the information comprising data relating to the mass, the aerodynamic resistance, the rolling friction, to pattern of the maximum torque developed by the engine of said vehicle, to the corresponding fuel flow maps, the fuel flow velocity (i.e. the flow rate of fuel) and the gear ratios.

Now, as it can easily be imagined one system like the one described above can be totally integrated into one vehicle or it can be only in part. Some versions are in fact foreseen, which hereinafter will be presented with reference to FIG. 7.

According to three main versions, the optimization modules 4 and 5 can be installed on board of the vehicle V or may be external to the vehicle V, for example installed, by means of a suitable software application on a computer device 6 in the cloud, with which they communicate through a suitable communication protocol and/or can be installed on an external device 3, for example a smartphone, tablet, personal computer, operatively connected by means of suitable communication interfaces and in a manner known to a person skilled in the art, to the other components of the system.

At least one of the above versions provides that the data of the vehicle V concerning in particular the engine are stored on board the vehicle itself, for example on an adjusted memory 7 of the non-volatile type. Such data, as said above, shall be measured or made available by the manufacturers of the vehicle V and/or the engine of the vehicle V as part of the specifications of the vehicle V and/or of the engine itself.

At least one of the above versions provides that at least the constant data of the vehicle V and/or the engine of the vehicle V (with constant a parameter that does not vary during the life of the vehicle V and/or the engine of the vehicle V is intended), are stored on board, for example by means of an adjusted non-volatile memory 7 suitable for the purpose. The parameters that the vehicle V and/or the engine of the vehicle V which vary during the life of the vehicle V and/or the engine, may instead be estimated in-line through estimation techniques such as Kalman filters, state observers, state estimators and the like. Reference is made in this case, to:

-   -   parameters of the vehicle V, such as the aerodynamic resistance         coefficient, the rolling friction coefficient, the masses, the         inertia, the characteristics of the chassis or other; and     -   engine parameters, such as the torque and/or power that can be         developed, the fuel consumption maps and any other indication of         the fuel quantity as a function of the operating conditions.

Alternatively, the method and the relative system according to the present invention can provide that the parameters of the vehicle V and/or engine, among those previously mentioned, which vary during the life cycle of the vehicle V and/or of the engine can be estimated not in-line and not on board the vehicle itself by an external system 8, for example during maintenance operations of the vehicle V and/or of the engine and then subsequently stored on board in an adjusted non-volatile memory, optionally on a external device 3, such as for example a smartphone, tablet or the like.

According to another version of the method and the corresponding system of the present invention, both the constant parameters and the ones that are not constant of the vehicle V and/or the engine may be stored on an external device 3 (for example a smartphone or tablet or the like), independently from the fact that they are constants or that they are estimated in-line.

In view of the above it can be clearly understood that a system like the one described above can be integrated in a control system of a vehicle (which is also an object of the present invention), including, inter alia, one or more control apparatuses configured to control the speed (ν) and the gear engaged (γ) (or the gear change (u_(sh)) for the vehicle V, operatively connected downstream of the system 2, from which they receive, in use, the reference signals determined according to the method described above.

Said one or more apparatuses for controlling the speed ν and the gear engaged γ (or of the gear changed u_(sh)) of the vehicle V, include, for example, a cruise control system which regulates a power-train, or a control unit of the gearbox or any other control device that contributes directly or indirectly to the control of the fuel consumption, the reduction of emissions, the safety while driving and similar.

The method and the system for obtaining reference signals described above fulfil the objects indicated above.

With particular reference to the method, experimental studies have shown that, under equal conditions with respect to traditional methods, the method according to the present invention provides for reference signals for vehicles control systems that allow to adjust the speed ν of a vehicle V, improving performance from the consumption point of view and reducing, at the same time, the travel times. In addition, the two optimization processes performed in sequence, and not at the same time, by the method according to the present invention, allows to considerably reduce the computational complexity of the method, thereby making it also more efficient from the computational point of view.

Below the results are shown of some tests carried out and illustrated in FIG. 8. FIG. 8 shows a comparison between the behaviour of the proposed method (OPT) as compared to the traditional method (CC), in the case of a vehicle with a full load, running the route whose profile in terms of altitude is reported in graph (a).

The traditional method implemented by a fixed point cruise control system (continuous line), acts on the gas control, on the basis of the residual between a reference speed (constant) and an actual speed. The gear changes are activated by the prediction of the current load, considering the maximum efficiency range of the engine in terms of revolutions per minute (rpm). Such a logic may generate, in some circumstances, an excessive series gear changes, leading to some disadvantages such as the loss of momentum, during the time interval in which the clutch disengages the engine from the wheels, with a consequent increase in fuel consumption on uphill sections of the route, and increased stresses on the transmission.

The method according to the present invention, on the other hand, (see the dashed lines in (b), (c) and (d) of FIG. 8), dynamically changes the reference speed and the engagement of the gears, based on the topography of the route, adapting to the profiles of the slope and, therefore, saving fuel.

By way of example, with reference to FIG. 8(b), it should be noted that the speed is slightly increased at approximately 3000 m, at the beginning of an uphill section, and at the same time the gear is predictively downshifted (Errore. L'origine riferimento non è stata trovata. (d)). The increased momentum is beneficial to tackle the next section at the higher speed, where the slope of the roadway increases rapidly. Furthermore, the gear change in advance allows to avoid to further change gear during the ascent. At the end of the uphill section, the traditional method provides for the quick return to the reference speed, while the method according to the present invention leaves the vehicle slowing down gently to exploit the next section of descent (4500-6000 m) for regaining speed.

At the end of the slope, the method according to the present invention maintains a higher speed compared to the traditional method, to compensate for the time lost in the preceding section.

Since this section is approximately plane, the method according to the present invention provides for a slight increase with reference to consumed fuel, which however is compensated by a gain in terms of journey time.

In this regard, in FIG. 9 the fuel consumption (ΔFC) is reported together with the variations of travel times (Δtime), in the case of a vehicle of 44 t at full load with different reference speeds, both for the traditional method (with reference speed constant—whose graphs are indicated with “Traditional Cruise Controller” in FIG. 9), both for the method according to the present invention (whose graphs are indicated with “Optimized” in FIG. 9).

The variations shown in the graph are related to the fuel consumption and the travel times obtained with a traditional method at reference speed set equal to 80 km/h on a stretch of road actually existing over more than 100 km.

As it can be noted, the method of the present invention allows, at equal reference speed, to save fuel and time.

Moreover, it is also possible to increase the reference speed to 82 km/h to save nearly 3% in terms of journey time, while maintaining a saving of fuel consumed of 1.37%. On the contrary, the increase of the reference speed to 82 km/h entails an increase in fuel consumption of 2.43% for the traditional method known as cruise control at a fixed point to, although it has a reduction in journey time of about 2%.

On the other hand, the reduction of the reference speed to 79 km/h allows for the method according to the present invention to save over the 4.04% of fuel, increasing the duration only of 0.70%.

In the foregoing the preferred embodiments were described and some modifications of this invention have been suggested, but it should be understood that those skilled in the art can make modifications and changes without departing from the relative scope of protection, as defined by the appended claims. 

1. Method for obtaining reference signals for systems for controlling a vehicle, in function of a geographical position of said vehicle along a route to travel, comprising the operating steps of: providing at least data relating to said vehicle and at least data relating to a route to travel; determining, on the basis of said data, at least one reference signal of a driving force F of said vehicle and at least one reference signal of a speed ν for said vehicle, in function of the position s of said vehicle along said route to travel, through a first optimisation process configured to optimise the driving force F along said route to travel; and determining at least one reference signal of an engaged gear γ and/or of gear change u_(sh) of said vehicle, in function of the position s of said vehicle along said route to travel, through a second optimisation process configured to optimise a fuel consumption of said vehicle along said route to travel; said second optimisation process being subsequent to said first optimisation process and receiving as input said data relating to said vehicle, said data relating to a route to travel, as well as said at least one reference signal of the driving force F and said at least one reference signal of the speed ν, determined through said first optimisation process.
 2. Method according to claim 1, wherein said data of said vehicle comprise information related to a mass, an aerodynamic resistance, a rolling friction, a curve of a torque developed by an engine, one or more fuel flow maps and transmission ratios of said engine, wherein optionally said data are constant or variable during the life of said vehicle and/or of said engine of said vehicle and wherein said variable data can be more optionally estimated in-line or not in-line.
 3. Method according to claim 1, wherein said data of said route comprise: an itinerary of said route to travel including a starting geographical position and a destination geographical position, a current geographical position, a slope and/or an elevation along said route to travel, and optionally one or more data selected from the group comprising data relating to speed limits established along said route to travel, an estimated arrival time or a desired maximum travel time, at least one desired average speed, at least one desired maximum speed and at least one desired minimum speed, at least one degree of adherence to said desired average speed, to said at least one desired maximum speed and to said at least one desired minimum speed, data relating to traffic conditions along said route to travel and data relating to weather conditions along said route to travel.
 4. Method according to claim 1, wherein said first optimisation process is an analytical process of minimisation of a cost function J(F) of the driving force F required to drive said vehicle having mass m along said route to travel.
 5. Method according to claim 4, wherein said minimisation of the cost function J(F) of the driving force F is given by the relationship: $\begin{matrix} {{\min\limits_{F}\left\{ {J(F)} \right\}} = {\min\limits_{F}\left\{ {\int_{0}^{S}{F^{2}{ds}}} \right\}}} & (1) \end{matrix}$ with the following state equation: $\begin{matrix} {{m\frac{dv}{dt}} = {F - F_{grade} - F_{drag} - F_{roll}}} & (2) \end{matrix}$ and the following constraints: $\begin{matrix} {v_{\min} \leq v \leq v_{\max}} & (3) \\ {F \leq F_{\max}} & (4) \\ {{{\int_{0}^{S}{\frac{1}{v}{ds}}} \leq T_{\max}} = \frac{S}{{\overset{\_}{v}}_{des}}} & (5) \end{matrix}$ where ν is the speed of said vehicle, s is the length travelled by said vehicle along said route of overall length S, ν_(min) is the desired minimum speed and ν_(max) is the desired maximum speed, F_(max) is the maximum available driving force of said vehicle, T_(max) is a maximum travel time and ν _(des) is the desired average speed, and taking account of exogenous forces as follows: F _(grade) =m g sin(α)   (6) F _(drag)=½ρ_(air) C _(x) Aν ²   (7) F _(roll) =m g cos(α)[C _(r0) +C _(r1) ν ²]  (8) where m is the mass of said vehicle (V), g is the gravitational acceleration, □ is an angle corresponding to the slope along said route to travel, ρ_(air) is an ambient air density along said route to travel, C_(x) is an aerodynamic resistance coefficient, A is a front area of said vehicle, C_(r0) is a first rolling resistance coefficient, and C_(r1) is a second rolling resistance coefficient.
 6. Method according to claim 5, wherein said analytical process of minimisation of the cost function J(F) of the driving force F performs a minimisation of the integral of the Lagrangian

(F), function of the driving force F, given by: $\begin{matrix} {{\mathcal{L}(F)} = {{\int_{0}^{S}{\left\{ {F^{2} + {{a(s)}\left\lbrack {\frac{dK}{ds} - F + {m\mspace{14mu} g\mspace{14mu} {\sin (\alpha)}} + {\frac{\rho_{air}\mspace{14mu} C_{x}\mspace{14mu} A}{m}K} + {m\mspace{14mu} g\mspace{14mu} {\cos (\alpha)}\left( {C_{r\; 0} - {C_{r\; 1}\frac{2\mspace{14mu} K}{m}}} \right)}} \right\rbrack} + {g_{1}(K)} + {g_{2}(K)} + {g_{3}\left( {F,{v(K)}} \right)} + {\lambda \left( {\frac{dt}{ds} - \sqrt{\frac{2\mspace{14mu} K}{m}}} \right)}} \right\} {ds}}} + {b_{1}\left( {{K(0)} - K_{0}} \right)} + {b_{2}\left( {{t(0)} - 0} \right)} + {\phi \left( {{t(S)} - T_{\max}} \right)}^{2}}} & (15) \end{matrix}$ where the functions g_(i), g₂ and g₃ are defined by the following equations: $\begin{matrix} {{g_{1}(K)} = \left\{ \begin{matrix} {c_{1}\left( {K - K_{\max}} \right)}^{2} & {{{if}\mspace{14mu} K} > K_{\max}} \\ {0\mspace{135mu}} & {{{if}\mspace{14mu} K} \leq K_{\max}} \end{matrix} \right.} & (16) \\ {{g_{2}(K)} = \left\{ \begin{matrix} {c_{2}\left( {K_{\min} - K} \right)}^{2} & {{{if}\mspace{14mu} K} < K_{\min}} \\ {0\mspace{135mu}} & {{{if}\mspace{14mu} K} \geq K_{\min}} \end{matrix} \right.} & (17) \\ {{g_{3}\left( {F,v} \right)} = \left\{ \begin{matrix} {c_{3}\left( {F - {F_{\max}(v)}} \right)}^{2} & {{{if}\mspace{14mu} F} > F_{\max}} \\ {0\mspace{160mu}} & {{{if}\mspace{14mu} F} \leq F_{\max}} \end{matrix} \right.} & (18) \end{matrix}$ where: K is a kinetic energy associated to the driving force F, K_(min) is a kinetic energy associated to the desired minimum speed ν_(min), K_(max) is a kinetic energy associated to the desired maximum speed ν_(max), c₁, c₂, c₃ are penalisation coefficients, t(s) is the travel time of said vehicle (V), φ is an amplitude of a quadratic penalty term, α(s) is the adjoint field, wherein the following constraints have to be satisfied $\begin{matrix} {\frac{dK}{ds} - F + {m\; g\; {\sin (\alpha)}} + {\frac{\rho_{air}C_{x}A}{m}K} - {m\; g\; {{\cos (\alpha)}\left\lbrack {C_{r0} - {C_{r\; 1}\frac{2K}{m}}} \right\rbrack}}} & (9) \\ {{\frac{1}{2}{mv}_{\min}^{2}} = {{K_{\min} \leq K \leq K_{\max}} = {\frac{1}{2}{mv}_{\max}^{2}}}} & (10) \\ {F \leq F_{\max}} & (11) \\ {\frac{dt}{ds} = \sqrt{\frac{m}{2K}}} & (13) \\ {{t(S)} = T_{\max}} & (14) \end{matrix}$ wherein said minimisation of the integral of the Lagrangian is executed by an algorithm of computation of the gradient of the integral of the Lagrangian $\frac{\delta\mathcal{L}}{\delta \; F},$ wherein said maximum available driving force F_(max) is optionally expressible as an analytical function approximating an envelope of the maximum driving force for each gear.
 7. Method according to claim 6, wherein said algorithm of computation of the gradient of the integral of the Lagrangian $\frac{\delta\mathcal{L}}{\delta \; F}$ is an adjoint-based iterative algorithm comprising the following steps: computing a gradient $\frac{\delta\mathcal{L}}{\delta \; F}$ of the Lagrangian

(F); iteratively using said gradient $\frac{\delta\mathcal{L}}{\delta \; F}$ of the integral of the Lagrangian

(F) to determine a search direction ρ_(k) along which searching a minimum of said gradient $\frac{\delta\mathcal{L}}{\delta \; F}$ of the integral of the Lagrangian

(F); executing a line minimisation; and repeating said iterations until at least one criterion to stop said iterations, indicating that a minimum of the integral of the Lagrangian

(F) has been reached, is satisfied wherein the penalisation coefficients c₁, c₂, c₃ are optionally constant or optionally progressively increased in correspondence of successive iterations, and wherein the amplitude φ of the quadratic penalty term is optionally constant or optionally dynamically modifiable in correspondence of successive iterations.
 8. Method according to claim 7, wherein said gradient $\frac{\delta\mathcal{L}}{\delta \; F}$ of the integral of the Lagrangian

(F) is given by: $\begin{matrix} {\frac{\delta\mathcal{L}}{\delta \; F} = {{- a} + {2F} + \frac{d\; g_{3}}{dF}}} & (22) \end{matrix}$ wherein said search direction ρ_(k) in correspondence of the k-th iteration of said adjoint-based iterative optimisation algorithm is given by: $\begin{matrix} \left\{ \begin{matrix} {p_{k} = \left. {- \frac{\delta\mathcal{L}}{\delta \; F}} \right|_{k}} & {{{if}\mspace{14mu} k} = 1} \\ {p_{k} = \left. {- \frac{\delta\mathcal{L}}{\delta \; F}} \middle| {}_{k}{{+ \beta_{k}}p_{k - 1}} \right.} & {{{if}\mspace{14mu} k} > 1} \end{matrix} \right. & (23) \end{matrix}$ where β represents the momentum of the search direction in correspondence of the preceding iteration, that is optionally determined through the Polak-Ribiére formula: $\begin{matrix} {\beta_{k} = {\max \left( {0;\frac{\left( \left. {- \frac{\delta\mathcal{L}}{\delta \; F}} \right|_{k} \right)^{T}\left( \left. {- \frac{\delta\mathcal{L}}{\delta \; F}} \middle| {}_{k}{+ \frac{\delta\mathcal{L}}{\delta \; F}} \right|_{k - 1} \right)}{\left( \left. {- \frac{\delta\mathcal{L}}{\delta \; F}} \right|_{k - 1} \right)^{T}\left( \left. {- \frac{\delta\mathcal{L}}{\delta \; F}} \right|_{k - 1} \right)}} \right)}} & (24) \end{matrix}$ wherein said line minimisation computes a step length h_(k) of the driving force F ensuring a maximum decrease of a cost function given by: F _(k+1) =F _(k) +h _(k)ρ_(k)   (25) iteratively solving a nonlinear equation for the scalar h given by: $\begin{matrix} {\frac{\partial{\mathcal{L}\left( {F + {hp}} \right)}}{\partial h} = {{\frac{\partial{\mathcal{L}\left( {F + {hp}} \right)}}{\partial\left( {F + {hp}} \right)}\frac{\partial{\mathcal{L}\left( {F + {hp}} \right)}}{\partial h}} = {\left( {{2\left( {F + {hp}} \right)} - a + \frac{d\; g_{3}}{dF}} \right) = 0}}} & (26) \end{matrix}$
 9. Method according to claim 7, wherein said at least one criterion to stop said iterations is given by: $\begin{matrix} \left\{ \begin{matrix} {\frac{{J\left( F_{k} \right)} - {J\left( F_{k - 1} \right)}}{J\left( F_{k - 1} \right)} \leq ɛ_{J}} \\ {\frac{{{t(S)}\text{|}_{k}} - {{t(S)}\text{|}_{k - 1}}}{{t(S)}\text{|}_{k - 1}} \leq ɛ_{t}} \end{matrix} \right. & (27) \end{matrix}$ where ε_(j) and ε_(t) are two predetermined threshold values.
 10. Method according to claim 1, wherein said second optimisation process is a dynamic process of minimisation of a cost function J₂ of the fuel consumption of said vehicle along said route to travel expressed as sum of M terms, where M is the number of segments having length Δs₂ forming said route to travel having length S, of the fuel consumption by said vehicle, along a i-th segment of length Δs₂ of said route of length S, and of penalising factors aimed at penalising gear changes on uphill and downhill sections.
 11. Method according to claim 10, wherein said minimisation of the cost function J₂ of the fuel consumption is given by the following relationship: $\begin{matrix} {{\min\limits_{\gamma_{i} \in \Gamma_{i}}\left\{ J_{2} \right\}} = {\min\limits_{\gamma_{i} \in \Gamma_{i}}\left\{ {\sum\limits_{i = 1}^{M}\; \left( {\frac{{W_{f,i}\left( \gamma_{i} \right)}\Delta \; s_{2}}{v_{i}} + {{\mu_{1,i}\left( {\alpha_{i},m} \right)}{u_{{sh},i}}} + {\mu_{2}{u_{{sh},i}}}} \right)} \right\}}} & (28) \end{matrix}$ with the following state equation: γ_(i+1)=γ_(i) +u _(sh,i)   (29) and the following constraints: γ_(i)ϵΓ_(i)   (30) Γ_(i):n_(e,min)≤n_(e)(γ_(i), K_(i))≤n_(e,max)   (31) u_(sh,i)ϵ[−2; −1; 0; +1; +2]  (32) where: $\begin{matrix} {M = \frac{S}{\Delta \; s_{2}}} & (33) \\ {\mu_{1,i} = \left\{ \begin{matrix} {m \cdot \alpha_{i}} & {{{if}\mspace{14mu} \alpha_{i}} > 0} \\ 0 & {{{if}\mspace{14mu} \alpha_{i}} \leq 0} \end{matrix} \right.} & (34) \end{matrix}$ and wherein: u_(sh,i) is a gear change signal in segment i; M is a number of summing terms of the cost function J₂ of the fuel consumption; W_(f,i) is a fuel flow rate in segment i; Δs₂ is a length of each one of the M segments; μ_(1,i) is a first penalisation coefficient that is variable in function of the mass m of the vehicle and of the slope α_(i) in segment i; μ₂ is a second penalisation coefficient that is constant; γ_(i) is the gear of the vehicle in segment i, included in a set of gears Γ_(i), n_(e) is the engine speed, ranging between a maximum value and a minimum value n_(e,max) and n_(e,min).
 12. Method according to claim 11, wherein fuel flow rate W_(f), function of the engine speed n_(e) and of the torque T_(e) developed by the engine, is expressed in function of said engaged gear γ as follows: $\begin{matrix} {{T_{e}\left( {F,\gamma} \right)} = \frac{{FR}_{w}}{i(\gamma)}} & (35) \\ {{n_{e}\left( {v,\gamma} \right)} = \frac{{vi}(\gamma)}{R_{w}}} & (36) \end{matrix}$ where i(γ) is a global transmission ratio, with the engaged gear γ, and R_(w) is a wheel radius of said vehicle, and F and ν are said at least one reference signal of the driving force and said at least one reference signal of a speed determined through said first optimisation process.
 13. System configured for obtaining reference signals for systems for controlling a vehicle, in function of a geographical position of said vehicle along a route to travel, comprising: at least one data collection device, configured to output at least data relating to said vehicle and at least data relating to said route to travel, and one or more processing units, configured to receive from said at least one data collection device said data relating to said vehicle and said data relating to said route to travel, said one or more processing units being configured to execute the method for obtaining reference signals for systems for controlling a vehicle according to claim
 1. 14. System configured for controlling the speed of a vehicle, comprising at least one system configured for obtaining reference signals according to claim 13 and one or more controlling apparatuses configured to control a speed of said vehicle and an engaged gear γ of said vehicle along said route to travel on the basis of reference signals received from said at least one system configured for obtaining reference signals.
 15. System according to claim 14, wherein said one or more controlling apparatuses comprise one or more apparatuses selected from the group comprising a cruise control system configured to control an engine unit, a unit controlling a gearbox of said vehicle and an apparatus configured to act on at least one braking device of said vehicle.
 16. (canceled)
 17. Set of one or more computer readable media, having stored thereon one set of one or more computer executable instructions that, when executed by one or more processing units, cause said one or more processing units to perform a method for obtaining reference signals for systems for controlling a vehicle, based on a geographical position of said vehicle along a route to travel, comprising the operating steps of: providing at least data relating to said vehicle and at least data relating to a route to travel: determining, on the basis of said data, at least one reference signal of a driving force F of said vehicle and at least one reference signal of a speed ν for said vehicle, in function of the position s of said vehicle along said route to travel, through a first optimisation process configured to optimise the driving force F along said route to travel; and determining at least one reference signal of an engaged gear γ and/or of gear change u_(sh) of said vehicle, in function of the position s of said vehicle along said route to travel, through a second optimisation process configured to optimise a fuel consumption of said vehicle along said route to travel; said second optimisation process being subsequent to said first optimisation process and receiving as input said data relating to said vehicle, said data relating to a route to travel, as well as said at least one reference signal of the driving force F and said at least one reference signal of the speed ν, determined through said first optimisation process. 